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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Ahdan, Syaiful Syambas, Nana Rachmana Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

The Named Data Networking (NDN) paradigm has been used as a promising vehicle communication model, namely the Vehicular Named Data Networking (V-NDN) model. In NDN, delivery of interest in the NDN network is done by selecting an existing interface. The problem is as to which interface is right in terms of fulfilling the request that suits the user. In this study, we compare the effectiveness of each forwarding strategy by creating new scenarios, such as variations in the number of interests and simulation time. We also measure the effectiveness of the forwarding strategy using the parameters specified in the NFD Forwarding Pipeline. The conclusion from the experiment is that superior forwarding strategy recommendations are based on content store (CS) management and data compatibility obtained based on consumer demand for producers. © 2023 IEEE.

关键词： Pipelines

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A novel medical image data protection scheme for smart healthcare system

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

Optimizing NOMA Transmissions to Advance Federated Learning ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Ziru Ni, Zhou Guan, Peiyuan Wang, Lu Cai, Lin X. Hashemi, Morteza Li, Zongzhi Illinois Institute of Technology Department of Electrical and Computer Engineering Chicago United States University of Kansas Department of Electrical Engineering and Computer Science Lawrence United States University of Oslo Department of Informatics Oslo Norway Illinois Institute of Technology Department of Civil Engineering Chicago United States

ISBN: (纸本)9798350351255

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms - Federated Vehicular Network, NOMA © 2024 IEEE.

关键词： Data privacy

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Enhancing Pretrained Multilingual Machine Translation Model with Code-Switching: A Study on Chinese, English and Malay Language 24

Enhancing Pretrained Multilingual Machine Translation Model ...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Liu, Haijing Seman, Noraini Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan China College of Computing Informatics and Mathematics University Technology MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9798400717482

In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource languages. This paper investigates how to leverage code-switching data to fine-tune pretrained multilingual machine translation model, in order to boost the performance of few-shot low resource machine translation. By utilizing a multilingual mixed corpus, the code-switching method can enhance the cross-linguistic generalization ability of the model and improve its overall understanding of the language. In this paper, on the smaller size model of FLORES-101 benchmark, we use the code-switching data augmentation method to achieve the results of benchmark's larger model on six direction pairs of three languages, Chinese, English and Malay. This paper studied various corpus mixture mechanisms to construct the data in code-switching, and the experimental findings show that the results using the code-switching fine-tuning model improve the spBLEU score by an average of 2 to 3 points over the results without code-switching. © 2024 Copyright held by the owner/author(s).

关键词： Machine translation

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Biernacki, Piotr Ziebinski, Adam Syu, Jia-Hao Lin, Jerry Chun-Wei Silesian University of Technology Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Gliwice Poland Silesian University of Technology Institute of Informatics Gliwice Poland National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors. © 2023 IEEE.

关键词： machine learning sensors calibration single-beam distance sensors

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Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

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computer Modeling in engineering & Sciences 2024年第2期138卷 1259-1275页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Heba M.El-Hoseny Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer science The Higher Future Institute for Specialized Technological StudiesEl ShoroukEgypt

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)***,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is *** this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift *** fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)*** the previous work,the probability of bit error is expressed in a closed-form *** is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano *** simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

关键词： Nanomedical system molecular communication cooperative relay optimization

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Selected Combinatorial Problems Through the Prism of Random Intersection Graphs Models 1

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13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Spirakis, Paul G. Nikoletseas, Sotiris Raptopoulos, Christoforos Department of Computer Science University of Liverpool Liverpool United Kingdom Computer Technology Institute Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (数字)9783031304484

ISBN: (纸本)9783031304477

We discuss a simple, yet general family of models, namely Random Intersection Graphs (RIGs), initially introduced by Karoński et al. [4] and Singer-Cohen [10]. In such models there is a universe of labels and each one of n vertices selects a random subset of. Two vertices are connected if and only if their corresponding subsets of labels intersect. A formal definition is given below: © 2023, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph theory

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Crowd Counting in Public Places Using MultiScale Convolutional Neural Network 24

Crowd Counting in Public Places Using MultiScale Convolution...

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24th International Electronics Symposium, IES 2022

作者： Karlita, Tita Mujir, Muhammad Ramadijanti, Nana Institute of Surabaya Electronic Engineering Polytechnic Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Crowd of people in public places is a serious problem that needs attention because uncontrolled crowd conditions will cause problems, especially with the Covidl9 pandemic which requires people not to congregate. This research uses the Multi Scale Convolutional Neural Network method to overcome the main problems in crowd images, namely object scale variations, difficulty distinguishing between people objects and the background, as well as overlapping between people objects. The Multi Scale CNN implementation in this research uses the feature extractor layer from VGG16 as the low level feature extractor layer (frontend layer) and the Inception-Restnet-A module from Inception-Resnet-v2 as the high level feature extractor (backend layer). The datasets used to train the model are the ShanghaiTech and UCF_QNRF datasets which already contain the location information of the people in the image. Prior to the training process, ground-truth was made by conducting a convolution process using a Gaussian filter at the point where people are. Then, the Multi Scale CNN model will be trained with these 2 datasets. In the trained model, the input image will be convoluted to produce a density map. The results of the crowd calculation are obtained by adding up all the density map values. The use of Multi Scale CNN is proven to provide a good accuracy value with the MAE loss value being 78.0 for the ShanghaiTech Part A dataset and 10.75 for the ShanghaiTech Part B dataset. © 2022 IEEE.

关键词： Convolutional neural networks

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Identification of the effect of different feature samples on Handwritten Japanese Character Recognition using YOLOv4 24

Identification of the effect of different feature samples on...

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24th International Electronics Symposium, IES 2022

作者： Karlita, Tita Adiputra, Andro Aprila Kusumaningtyas, Entin Martiana Institute of Surabaya Electronic Engineering Polytechnics Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Handwritten Japanese character recognition has been one of the major tasks in the computer vision field. Previously, various approaches to distinguish characters had proposed, ranging from traditional machine learning, end-to-end model, images retrieval and object detection model. Most research focuses on model performances in ideal conditions with a similar feature distribution and produces an excellent result. In the character recognition task, one of the most efficient ways is to utilize an end-to-end object detection model to avoid sequences and robust against different feature distributions. YOLOv4 is one of the end-to-end object detection models that applies various concepts such as a bag of freebies and bag of specials to increase the robustness of the model by improving the context generalization. In this research, our target is to identify the effect of different feature distributions applied to ideal datasets using YOLOv4 on Japanese character recognition tasks. The result of classification reveals both F1-score and Map accuracy of mAP@50 in the ideal dataset achieve 99%. While in the combined dataset, the performance degradation is 12% lower compared to the ideal dataset. YOLOv4 backbone has proven to be robust generalize different feature distributions. © 2022 IEEE.

关键词： Object detection

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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